Automatic calibration of a masking process simulator

ABSTRACT

A method and system is provided for automatically calibrating a masking process simulator using a calibration mask and process parameters to produce a calibration pattern on a wafer. A digital image is created of the calibration pattern, and the edges of the pattern are detected. Data defining the calibration mask and at least one of the process parameters are input to a process simulator to produce an alim image estimating the calibration pattern that would be produced by the masking process. The alim image and the detected edges of the digital image are then overlaid, and a distance between contours of the pattern in the alim image and the detected edges is measured. One or more mathematical algorithms are used to iteratively change the values of the processing parameters until a set of processing parameter values are found that produces a minimum distance between the contours of the pattern in the alim image and the detected edges.

This is a continuation of application Ser. No. 10/305,673 filed Nov. 26,2002, now U.S. Pat. No. 6,768,958.

FIELD OF THE INVENTION

The present invention relates to the field of semiconductor processingand more particularly to an improved process for automaticallycalibrating a masking process simulator.

BACKGROUND OF THE INVENTION

An integrated circuit is fabricated by translating a circuit design orlayout to a semiconductor substrate. In optical lithography, the layoutis first transferred onto a physical template, which is in turn used tooptically project the layout onto a silicon wafer. In transferring thelayout to a physical template, a mask is generally created for eachlayer of the integrated circuit design. The patterned photomask includestransparent, attenuated phase shifted, phase shifted, and opaque areasfor selectively exposing regions of the photoresist-coated wafer to anenergy source. To fabricate a particular layer of the design, thecorresponding mask is placed over the wafer and a stepper or scannermachine shines a light through the mask from the energy source. The endresult is a semiconductor wafer coated with a photoresist layer havingthe desired pattern that defines the geometries, features, lines andshapes of that layer. The photolithography process is typically followedby an etch process during which the underlying substrate not covered ormasked by the photoresist pattern is etched away, leaving the desiredpattern in the substrate. This process is then repeated for each layerof the design.

Ideally, the photoresist pattern produced by the photolithographyprocess and the substrate pattern produced by the subsequent etchprocess would precisely duplicate the pattern on the photomask. For avariety of reasons, however, the photoresist pattern remaining after theresist develop step may vary from the pattern of the photomasksignificantly. Diffraction effects and variations in thephotolithography process parameters typically result in criticaldimension (CD) variation from line to line depending upon the line pitchof the surrounding environment (where pitch is defined for purposes ofthis disclosure as the displacement between an adjacent pair ofinterconnect lines). In addition to CD variation, fringing effects andother process variations can result in end-of-line effects (in which theterminal end of an interconnect line in the pattern is shortened or cutoff by the photolithography process) and corner rounding (in whichsquare angles in the photomask translate into rounded corners in thepattern). These three primary optical proximity effects, together withother photoresist phenomena such as notching, combine to undesirablyproduce a patterned photoresist layer that may vary significantly fromthe pattern of the photomask. In addition to variations introducedduring the photolithography process, further variations and distortionsare typically introduced during the subsequent etch process such thatthe pattern produced in the semiconductor substrate may vary from thephotomask pattern even more than the photoresist pattern.

Conventional semiconductor process engineering in the areas ofphotolithography and etch typically attempts to obtain a finishedpattern that approximates the desired pattern as closely as possible bycontrollably altering the process parameters associated with the variousmasking steps. Among the parameters process engineers typically attemptto vary in an effort to produce a photoresist pattern substantiallyidentical to the photomask pattern include the intensity, coherency andwave length of the energy source, the type of photoresist, thetemperature at which the photoresist is heated prior to exposure(pre-bake), the dose (intensity×time) of the exposing energy, thenumerical aperture of the lens used in the optical aligner, the use ofantireflective coatings, the develop time, developer concentration,developer temperature, developer agitation method, post baketemperature, and a variety of other parameters associated with thephotolithography process. Etch parameters subject to variation mayinclude, for example, process pressure and temperature, concentrationand composition of the etch species, and the application of a radiofrequency energy field within the etch chamber.

Despite their best efforts, however, semiconductor process engineers aretypically unable to manipulate the photolithography and etch processessuch that the photoresist and substrate patterns produced by theprocesses are substantially identical to the photomask pattern.

To avoid the time and cost of producing actual test wafers for everydesired permutation of process parameters, computerized simulation ofmasking processes is employed to facilitate the optimization of aparticular masking sequence and the generation of an optical proximitycorrection (OPC) distorted photomask. Masking process simulators receivevarious inputs corresponding to the parameters of the photoresist andetch processes to be simulated and attempt to simulate the pattern thatwill be produced by the specified masking process given a particularphotomask. Accordingly, computerization has significantly enhanced theprocess engineer's ability to characterize and optimize maskingprocesses.

Nevertheless, it is typically impossible to adequately account for themultitude of parameters associated with a masking process despite theeffort devoted to masking process characterization, the introduction ofoptical proximity correction techniques, and the emergence ofsophisticated process simulation software. In other words, simulationprograms are ultimately unable to account for the various parametricdependencies in a manner sufficient to predict the exact pattern thatwill be produced by any particular masking process and mask.

Accordingly, what is needed is a method and system for improving theprediction accuracy of masking process simulator software. The presentinvention addresses such a need.

SUMMARY OF THE INVENTION

The present invention provides a method and system for improving theprediction accuracy of masking process simulators through automaticcalibration of the simulators. The method and system include performinga masking process using a calibration mask and process parameters toproduce a calibration pattern on a wafer. A digital image is created ofthe calibration pattern, and the edges of the pattern are detected fromthe digital image using pattern recognition. Data defining thecalibration mask and the process parameters are then input to a processsimulator to produce an alim image estimating the calibration patternthat would be produced by the masking process. The method and systemfurther include overlaying the alim image and the detected edges of thedigital image, and measuring a distance between contours of the patternin the alim image and the detected edges. Thereafter, one or moremathematical algorithms are used to iteratively change the values of theprocessing parameters input to the simulator until a set of processingparameter values are found that produces a minimum distance between thecontours of the pattern in the alim image and the detected edges.

According to the method and system disclosed herein, the calibrationeffectively calibrates the process simulator to compensate for processvariations of the masking process. Once the calibration is performed andactual mask data and the modified process parameters are input to theprocess simulator, the process simulator will produce an image thatvaries minimally from the actual pattern produced by the maskingprocess.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing a portion of a desired semiconductor patternand the patterned layer resulting from the masking process.

FIGS. 2A and 2B are flow charts illustrating a process for calibrating aprocess simulator to compensate for process variations of the maskingprocess in accordance with a preferred embodiment of the presentinvention.

FIG. 3 is a block diagram of a web-enabled process simulation system ina preferred embodiment of the present invention.

FIG. 4 is an illustration of an example calibration mask pattern.

FIG. 5 is an illustration of an example SEM image produced by themasking process using the mask design shown in FIG. 4.

FIG. 6 is a diagram showing an alim image superimposed with the detectedSEM edges.

FIG. 7 is a diagram illustrating a user interface screen produced by thecalibration program in a preferred embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to simulating semiconductor fabricationprocesses and a method for improving process simulators throughautomatic calibration. The following description is presented to enableone of ordinary skill in the art to make and use the invention and isprovided in the context of a patent application and its requirements.Various modifications to the preferred embodiments and the genericprinciples and features described herein will be readily apparent tothose skilled in the art. Thus, the present invention is not intended tobe limited to the embodiments shown, but is to be accorded the widestscope consistent with the principles and features described herein.

Referring now to FIG. 1 a portion of a desired semiconductor pattern andthe patterned layer resulting from the masking process is shown. Thesemiconductor pattern shown in dashed lines includes various patternelements 102 a, and 102 b (collectively referred to as pattern elements102). Using the pattern, a masking process is used to create thepatterned layer 131, comprising the actual elements 132. The patternedlayer 131 may comprise, in alternative embodiments, a photoresistpattern produced by a photolithography process or a substrate patternproduced by an etch process.

As will be appreciated to those skilled in the art of semiconductorprocessing and design, elements 102 of semiconductor pattern includesvarious interconnect sections and pattern elements designed to achieve adesired function when the integrated circuit contemplated by thesemiconductor fabrication process is completed. Typical elements 102 ofa semiconductor pattern are substantially comprised of straight linesand square corners. For a variety of reasons, reproducing the exactimage of semiconductor pattern in a production process is extremelycomplicated due to the large number of parameters associated withtypical masking processes and further due to the unavoidable diffractioneffects which inevitably result in some variations between the photomaskused to produce a pattern and the pattern itself.

It is seen in FIG. 1 that the actual pattern 131 produced by a maskingprocess varies from the desired semiconductor pattern 102. Thisdiscrepancy is shown in FIG. 1 as the displacement between the dashedlines of pattern elements 102 a and 102 b and the actual patternelements 132 a and 132 b. Typically, the variations from the idealizedpattern 102 include rounding of the corners and a shrinking of the linewidths. It will be appreciated to those skilled in the art ofsemiconductor processing that variations from the desired semiconductorpattern can contribute to lower processing yields, reduced reliability,reduced tolerance to subsequent misalignment, and other undesiredeffects.

As is well-known in the art, commercial masking process simulationsoftware is available that is capable of producing a simulated estimateof the pattern that would be produced by a specified masking processusing a given photomask. Examples of process simulation software includeTSUPREM-4™ and Taurus-LRC™ by Synopsys, Inc. of Mountain View, Calif.Masking process simulators are useful for generating a large quantity ofinformation concerning the effects of modifying various parametersassociated with the process. Simulation is necessary to avoid the timeand expense associated with producing actual test wafers for eachproposed parameter modification.

Ultimately, the simulator will produce an estimate of the pattern,referred to as an aerial or latent image, that varies from the actualpattern produced by the masking process (due to diffraction effects andvariations in the masking process) regardless of the number ofparameters incorporated into the simulator.

The Assignee of the present application has developed a process thatimproves the process simulator's prediction of the final patternproduced by a masking process by using the actual results obtainedgenerated by the masking process. For example, U.S. Pat. Nos. 6,078,738and 6,081,659, which are hereby incorporated by reference, discloses aprocess that introduces a feedback mechanism into the simulation processwhereby the discrepancies observed between the actual pattern and theaerial image are analyzed to produce a modified simulator that resultsin less discrepancy, or error between the aerial image produced during asuccessive iteration of the simulator and the actual image produced bythe pattern.

Using the actual results obtained by the masking process to improve theprediction accuracy of the process simulator program can be referred toas a calibration process. However, how the calibration is implemented,including how the simulator is modified based on the calibration, cansignificantly affect the performance of the simulator.

One approach for modifying the simulator during the calibration is amanual process whereby an operator iteratively changes the processparameter values input to the simulator by hand until the simulatorachieves a desired level of performance. It is difficult, however, forthe operator to change more than a couple of the processing parameter ata time, making the process tedious, error prone, and time-consuming.

Another calibration approach uses critical dimension checking whereby acritical dimension of a particular feature of the actual patternproduced by the masking process is measured directly from a productionwafer. The same critical distance is also measured across the feature inthe aerial image produced by simulator. The processing parameters inputto the simulator are then changed using an exhaustive search algorithmuntil the simulator produces an aerial image that has a criticaldistance equal to that of the actual pattern. One disadvantage of thismethod is that the critical dimension typically measures a feature inone dimension only, typically horizontally or vertically across themiddle of the feature. The process, therefore, is incapable of analyzingthe pattern in areas where most stepper errors occur, such as the endsof lines and the spaces between features.

Accordingly, the present invention provides an improved process foranalyzing the difference between the aerial image produced by asimulator and the actual pattern produced by the masking process inorder to provide an improved method for calibrating the simulator.According to the present invention, calibration mask data and processparameters are input to a process simulator to produce an aerial imageestimating the calibration pattern that would be produced by a maskingprocess. The same calibration mask data and process parameters are usedduring the masking process to produce an actual calibration pattern on awafer. A digital image of the actual pattern on the wafer is obtained,preferably using a scanning electron microscope (SEM). The edges of thepattern are automatically recognized from the SEM image using patternrecognition, and the recognized edges of the actual pattern aresuperimposed with the pattern in the aerial image. The distance betweenthe contours of the pattern in the aerial image and the countours of theSEM edges is then measured, providing a distance value that is based ontwo dimensions, rather than one. One or more mathematical algorithms arethen used to iteratively change the values of processing parametersinput to the simulator until a set of processing parameter values arefound that produces a minimum distance between the aerial image contoursand SEM edges. This new set of parameters effectively calibrate theprocess simulator to compensate for process variations of the maskingprocess.

Once the calibration is performed and an operator inputs actual maskdata and the modified process parameters into the process simulator, theprocess simulator will produce an aerial image that varies minimallyfrom the actual pattern produced by the masking process. The calibratedprocess simulator may be used for a variety of tasks includingpredicting mask defects, as a model for OPC correction, for phaseshifting mask correction, and so on.

FIGS. 2A and 2B are flow chart illustrating a process for calibrating aprocess simulator to compensate for process variations of the maskingprocess in accordance with a preferred embodiment of the presentinvention. The process begins in step 50 by providing a processsimulation program that operates in accordance with the presentinvention on a server, and making the program available over a network,such as the Internet.

FIG. 3 is a block diagram of a web-enabled process simulation system ina preferred embodiment of the present invention. The simulation system140 includes a process simulator 142 and an automatic calibrationprogram 143 for calibrating the process simulator 142. The processsimulator 142 and the automatic calibration program 143 are executed ona server 144 as application programs and accessed over a network 146 byone or more operators using client computers 150. The automaticcalibration program 143 may be included as part of, or separate from theprocess simulator 142.

The process simulator 142 and calibration program 143 are capable ofaccessing one or more mask layout databases 152, each of which includesa set of mask designs that will be used to fabricate a particularsemiconductor device. In particular, the calibration process 143typically accesses a calibration mask design (not shown) whencalibrating the process simulator 142. The process simulation system 140also includes a data set 154 defining the input processing parameters,as described below. FIG. 4 is an illustration of an example calibrationmask pattern from the mask layout database 152. In a preferredembodiment, mask data is stored in GDSII format.

Referring again to FIG. 2, a calibration pattern is fabricated on awafer by a masking process in step 52 whereby a physical calibrationmask and a stepper machine are used to generate the calibration patternunder the conditions specified by the data set 154. The data set 154includes global processing parameters that are associated with themasking process. In a preferred embodiment, the global processingparameters include both resist parameters for simulating thephotoresist, and optical parameters for simulating the optics andcharacteristics of stepper machine.

As is well known to those skilled in the field of photolithographyengineering, examples of resist parameters include resist contrast(gamma), resist thickness, resist sensitivity, resist solids content,and resist viscosity. Examples of the optical parameters that may affectthe resist image include the intensity of the stepper lamp, the durationof the exposure, the coherency of the optical energy, the aperture oflenses, and the wavelength of the lamp source. It will be furtherappreciated to those skilled in the art, that the develop process andthe etch process both include a number of parameters that may also beinput to the process simulator 142, such as develop time, developerconcentration, developer temperature, developer agitation method, andany post bake time and temperature. Etch parameters may include, forexample, etch temperature, etch pressure, and etchant composition andconcentration. The process parameters described above are meant to beillustrative rather than exhaustive and additional parameters may beincorporated into the simulator 142.

After the physical calibration pattern is fabricated by the maskingprocess, a scanning electron microscope (SEM) is used to create adigital representation of the pattern, referred to herein as an SEMimage in step 54. FIG. 5 is an illustration of an example SEM imageproduced by the masking process using the mask design shown in FIG. 4.

Referring again to FIG. 2, according to one aspect of the presentinvention, in step 56, the edges of the mask pattern in the SEM imageare automatically detected using pattern recognition. The detected edgesmay be stored in an edge database in a standard format, such as GDSII (astandard file format for transferring/archiving to the graphic designdata). In one preferred embodiment, an algorithm, referred to as a SnakeAlgorithm, is used to automatically detect the mask edges from the SEMimage, as disclosed in U.S. patent application Ser. No. 10/251,082entitled “Mask Defect Analysis for Both Horizontal and VerticalProcessing Effects” (2513P) filed on Sep. 20, 2002 by the presentassignee and herein incorporated by reference. In an alternativeembodiment, an “Adaptive SEM Edge Recognition Algorithm” may also beused to detect the edges, as disclosed in U.S. Ser. No. 10/327,452,entitled “Adaptive SEM Edge Recognition Algorithm,” filed on December2002.

In step 58, the SEM image is correlated to the GDS mask design datalayout database 152 in order to determine how many pixels in the SEMimage are equal to one unit of measure of the mask design, which istypically nanometers.

In step 60, an operator of a client computer 150 invokes the calibrationprogram 143. In step 62, the operator selects the calibration maskdesign and the data set 154 representing global processing parameters ofthe masking process that were used to fabricate a calibration pattern.

In step 64, the calibration mask data and process parameters are inputto the process simulator 142 to produce an image estimating thecalibration pattern that would be produced by a masking process. As iswell-known in the art, an aerial or latent image may be produced by thesimulator, which are collectively referred to herein as an “alim” image(aerial/latent image). The alim images generated by the processsimulator 142 may be stored either on a server, or on the clientcomputer 150.

In step 66, the alim image, the calibration mask design, and thedetected SEM edges are overlaid. In step 68, the alignment between thealim image, the calibration mask, and the detected SEM edges arerefined. In a preferred embodiment, the alim image and the pattern inthe SEM image may include corresponding alignment marks to facilitate asubsequent alignment and comparison. The overlaid images may optionallybe displayed to the operator. FIG. 6 is a diagram showing an examplealim image 164, shown with white lines, superimposed with the detectedSEM edges, shown with dark lines.

Referring again to FIG. 2, in step 70, the distance between the alimimage contours and the detected SEM edges are determined. In a preferredembodiment, this distance is determined using a root mean square (RMS)algorithm. The RMS algorithm measures the distance between each pair ofcorresponding edges in the alim image 164 and the SEM image 166 (or asubset of the edges) and applies a weighted average to the measureddistances to produce a single distance value. In another preferredembodiment, the weighted average is equal to an Nth root of an averageNth power of distance between the SEM edges and the alim image for someN not necessarily equal to 2. Calculating distances between the contoursin this manner effectively provides a distance value that is based ontwo-dimensional measurements.

In step 72, one or more mathematical algorithms are used to search for aset of processing parameter values for input to the simulator that willproduce the minimum distance between the alim image contours and the SEMedges. The operator may also define a minimum distance threshold thatwill be used to terminate the search, and the minimum and maximumpossible values for the processing parameters.

In a preferred embodiment, a subset of the processing parameters used bythe masking process are input to the mathematical algorithm. Accordingto the present invention, the following 11 processing parameters areused to determine the minimum distance: focus, diffusion, sigma in,sigma out, angle of the pole location, numerical aperture, sigma of thepole, spherical, coma_x, coma_y, and intenstity contour.

In step 74, it is determined if the calculated distance between the alimimage contours and the SEM edges meets the minimum distance thresholdset by the operator. The minimum distance threshold is dependent uponthe particular process technology being used. For a 130 nm processtechnology, for example, the minimum distance threshold may be set at8-10 nm, which means that the process simulator must produce an alimimage 164 that is within 10% of an SEM image 166. For criticalapplications, an error threshold of 5% or less may be necessary.

If the calculated distance does not meet the minimum distance threshold,then the algorithm calculates new values for the processing parametersin step 76. The new processing parameter values are calculated duringthe process of minimizing the distance between the alim image contoursand the SEM edge contours given a function (f) of the 11 variables (x):f(x₁. . . , x₁₁)/R¹¹−R

In a preferred embodiment, two algorithms are employed to minimize thisequation. First, a well-known stochastic algorithm is used toiteratively change the processing values until a global minimum for thefunction is found. This first set of calculated parameter values thatproduce the global minimum are then input to a second well-knowalgorithm, referred to as a simplex or Powell algorithm. This algorithmbegins with the function defined by this set of parameter values anditeratively changes the values of the parameters until local minimumswithin the function are found, producing a second set of parametervalues.

In step 78, this second set of calculated parameter values are input tothe process simulator 142 to generate a new alim image 164, and theprocess continues with steps 66-72. The alim image 164 is overlaid withthe SEM edges and the distance between the two are calculated, etc. Ifthe calculated distance between the alim image contours and the SEM edgecontours does not meet the minimum distance threshold in step 74, thenthe process continues. If the calculated distance between the alim imagecontours and the SEM edge contours meets the minimum distance thresholdin step 74, then in step 80 the current set of parameter values are theoptimal set of parameters and are output by calibration program 143 forcalibration of the process simulator 142.

FIG. 7 is a diagram illustrating a user interface screen produced by thecalibration program in a preferred embodiment of the present invention.In a further aspect of the present invention, the calibration programuser interface screen 170 displays individual graphs 172 for eachprocessing parameter that plot the parameter values for each iterationalong the x-axis, and the resulting RMS distance value along the y-axis.In addition to the individual parameter graphs 172, the user interfacescreen also displays a global graph 174 plotting the global RMS distanceresult of each iteration.

A method and system for calibrating a process simulator have beendisclosed. The present invention has been described in accordance withthe embodiments shown, and one of ordinary skill in the art will readilyrecognize that there could be variations to the embodiments, and anyvariations would be within the spirit and scope of the presentinvention. Accordingly, many modifications may be made by one ofordinary skill in the art without departing from the spirit and scope ofthe appended claims.

1. A computer-implemented method for automatically calibrating a maskingprocess simulator, comprising: (a) performing a masking process using acalibration mask and process parameters to produce a calibration patternon a wafer; (b) creating a digital image of the calibration pattern; (c)detecting edges of the pattern from the digital image using patternrecognition; (d) inputting data defining the calibration mask and atleast one of the process parameters into a process simulator to producean alim image estimating the calibration pattern that would be producedby the masking process; (e) overlaying the alim image and the detectededges of the digital image; (f) measuring a distance between contours ofthe pattern in the alim image and the detected edges; and (g) using oneor more mathematical algorithms to iteratively change values of the atleast one processing parameter input to the simulator until a processingparameter value is found that produces a minimum distance between thecontours of the pattern in the alim image and the detected edges,thereby effectively calibrating the process simulator to compensate forprocess variations of the masking process.
 2. The method of claim 1wherein step (b) further includes: using a scanning electron microscope(SEM) to create an SEM image of the calibration pattern.
 3. The methodof claim 2 wherein step (g) further includes: using multiple processingparameters and changing the values of a subset of the processingparameters.
 4. The method of claim 3 wherein the subset of processingparameters includes focus, diffusion, sigma in, sigma out, angle of thepole location, numerical aperture, sigma of the pole, spherical, coma_x,coma_y, and intenstity contour.
 5. The method of claim 3 wherein step(g) further includes: receiving from an operator a minimum distancethreshold that will be used to terminate a search for the processingparameters, and the minimum and maximum possible values for theprocessing parameters.
 6. The method of claim 5 wherein step (g) furtherincludes: (i) using a first algorithm to iteratively change theparameter values until a global minimum for a function of the processingparameters is found; (ii) inputting a first set of calculated parametervalues that produced the global minimum to a second algorithm, whereinthe second algorithm begins with the function defined by the first setof parameter values and iteratively changes the values of the parametersuntil local minimums within the function are found, producing a secondset of parameter values.
 7. The method of claim 6 wherein the firstalgorithm is a stochastic algorithm.
 8. The method of claim 6 whereinstep (g) further includes: iteratively inputting the second set ofcalculated parameter values into the process simulator to generate newalim images for the distance measurement.
 9. The method of claim 3wherein step (c) further includes: correlating the SEM image to the maskdesign data in order to determine how many pixels in the SEM image areequal to one unit of measure of the mask design.
 10. The method of claim3 wherein step (f) further includes: determining the distance using adistance metric, including root mean square (RMS) algorithm.
 11. Themethod of claim 10 wherein the distance metric measures a distancebetween at least a subset of each pair of corresponding edges in thealim image and the SEM image and applies a weighted average to themeasured distances to produce a single distance value.
 12. The method ofclaim 11 wherein the weighted average is equal to an Nth root of anaverage Nth power of distance between the SEM edges and the alim image.13. The method of claim 3 wherein the alim image comprises an aerialimage or a latent image.
 14. The method of claim 1 further including:displaying a user interface screen that displays a graph for the atleast one processing parameter that plots parameter values for eachiteration along with resulting distance values, and displays a globalgraph plotting a global distance result of each iteration.
 15. Themethod of claim 1 wherein the calibration mask is produced by inputtinginto the masking process global processing parameters, which includeboth resist parameters for simulating photoresist and optical parametersfor simulating optics and characteristics of a stepper machine.
 16. Aprocess simulator system, comprising: a server coupled to a network; acalibration program executing on the server; a process simulatorexecuting on the server; and at least one client computer coupled to theserver over the network, such that an operator may access thecalibration program, wherein once invoked, the calibration program: (a)receives a digital image of a calibration pattern on a wafer, thecalibration pattern produced during a masking process using acalibration mask and process parameters (b) detects edges of the patternfrom the digital image using pattern recognition; (c) inputs datadefining the calibration mask and at least one of the process parametersinto the process simulator, which then produces an alim image estimatingthe calibration pattern that would be produced by the masking process;(d) overlays the alim image and the detected edges of the digital image;(e) measures a distance between contours of the pattern in the alimimage and the detected edges; and (f) uses one or more mathematicalalgorithms to iteratively change values of the at least one processingparameter input to the simulator until a processing parameter value isfound that produces a minimum distance between the contours of thepattern in the alim image and the detected edges, thereby effectivelycalibrating the process simulator to compensate for process variationsof the masking process.
 17. The system of claim 16 wherein a scanningelectron microscope (SEM) is used to create an SEM image of thecalibration pattern.
 18. The system of claim 17 wherein multipleprocessing parameters are used and the mathematical algorithmsiteratively change the values of a subset of the processing parameters.19. The system of claim 18 wherein the subset of processing parametersincludes focus, diffusion, sigma in, sigma out, angle of the polelocation, numerical aperture, sigma of the pole, spherical, coma_x,coma_y, and intenstity contour.
 20. The system of claim 18 wherein thecalibration program receives from the operator a minimum distancethreshold that will be used to terminate the search by the mathematicalalgorithms, and the minimum and maximum possible values for theprocessing parameters.
 21. The system of claim 20 wherein themathematical algorithms include (i) a first algorithm for iterativelychanging the parameter values until a global minimum for a function ofthe processing parameters is found; and (ii) a second algorithm thatreceives a first set of calculated parameter values that produced theglobal minimum and begins with a function defined by the first set ofparameter values, and iteratively changes the values of the parametersuntil local minimums within the function are found, producing a secondset of parameter values.
 22. The system of claim 21 wherein the firstalgorithm is a stochastic algorithm.
 23. The system of claim 21 whereinthe calibration program iteratively inputs the second set of calculatedparameter values into the process simulator to generate new alim imagesfor the distance measurement.
 24. The system of claim 17 wherein the SEMimage is correlated to the mask design data in order to determine howmany pixels in the SEM image are equal to one unit of measure of themask design.
 25. The system of claim 17 wherein the distance isdetermined using a distance metric, including a root mean square (RMS)algorithm.
 26. The system of claim 25 wherein the distance metricmeasures a distance between at least a subset of each pair ofcorresponding edges in the alim image and the SEM image, and applies aweighted average to the measured distances to produce a single distancevalue.
 27. The system of claim 26 wherein the weighted average is equalto an Nth root of an average Nth power of distance between the SEM edgesand the alim image.
 28. The system of claim 17 wherein the alim imagecomprises an aerial image or a latent image.
 29. The system of claim 16wherein the calibration program displays a user interface screen thatdisplays individual graphs for each processing parameter that plotparameter values for each iteration along with resulting distancevalues, and displays a global graph plotting a global distance result ofeach iteration.
 30. The system of claim 16 wherein processing parametersinput to the process simulator comprise global processing parameters,which include both resist parameters for simulating photoresist andoptical parameters for simulating optics and characteristics of astepper machine.